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Related Experiment Video

Updated: May 10, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

428

Multi-Scale Fusion Lightweight Target Detection Method for Coal and Gangue Based on EMBS-YOLOv8s.

Lin Gao1, Pengwei Yu1, Hongjuan Dong2

  • 1Mechanical Engineering School, Inner Mongolia University of Science and Technology, Baotou 014010, China.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
Summary

This study introduces EMBS-YOLOv8s, a lightweight coal gangue detection model that enhances accuracy and efficiency. The improved model achieves 96.0% mean average precision, outperforming the original YOLOv8s while reducing complexity and increasing detection speed for intelligent coal sorting.

Keywords:
CLAHEWise-SIoU loss functionYOLOv8scoal gangue detectionefficient multi-branch and scale feature pyramid network (EMBSFPN)

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Accurate coal gangue detection is crucial for intelligent coal sorting.
  • Existing methods suffer from low accuracy and complex model structures.
  • There is a need for efficient and precise coal gangue detection systems.

Purpose of the Study:

  • To propose a multi-scale fusion lightweight coal gangue target detection method.
  • To enhance the accuracy and reduce the complexity of coal gangue detection models.
  • To develop a model suitable for real-time intelligent sorting applications.

Main Methods:

  • Utilized Contrast Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing.
  • Implemented an Enhanced Multi-scale Bidirectional Feature Pyramid Network (EMBSFPN) in the neck network.
  • Replaced the CIoU loss function with Wise-SIoU loss for improved convergence and sample balancing.

Main Results:

  • The EMBS-YOLOv8s model achieved a mean average precision of 96.0% on a custom dataset.
  • Model complexity was reduced: Params (29.59%), FLOPs (12.68%), and Size (28.44%) compared to YOLOv8s.
  • Achieved a detection speed of 93.28 frames per second, demonstrating real-time capabilities.
  • Effectively minimized false and missed detections in challenging conditions like low illumination and motion blur.

Conclusions:

  • The proposed EMBS-YOLOv8s model offers superior performance in coal gangue detection.
  • The method provides a lightweight, accurate, and efficient solution for intelligent coal sorting.
  • EMBS-YOLOv8s demonstrates robustness in complex and adverse imaging scenarios.